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Activity Number: 408 - Methods for Single-Cell Genomic Analysis
Type: Contributed
Date/Time: Tuesday, August 1, 2017 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #323589
Title: Missing Data and Technical Variability in Single-Cell RNA-Sequencing Experiments
Author(s): Stephanie Hicks* and Mingxiang Teng and F. William Townes and Rafael Irizarry
Companies: Dana-Farber Cancer Institute/Harvard T.H. Chan School of Public Health and Dana-Farber Cancer Institute/Harvard T.H. Chan School of Public Health and Harvard T.H. Chan School of Public Health and Harvard School of Public Health
Keywords: gene expression ; missing not at random ; single-cell RNA-Seq
Abstract:

Recent advances in high-throughput technology permit genome-wide gene expression measurement at the single cell level. Single-cell RNA-Sequencing (scRNA-Seq) is the most widely and has been used in numerous publications. Although systematic errors, including batch effects, have been widely reported as a major challenge in high-throughput technologies, these issues have received minimal attention in published studies based on scRNA-Seq technology. Here, we examine data from published studies and found that systematic errors can explain a substantial percentage of observed cell-to-cell expression variability. Specifically, we show that scRNA-Seq reports more zeros than expected, and that technical variability can lead to cell-to-cell differences that can be confused with novel biological results. Finally, we demonstrate how batch-effects can exacerbate the problem.


Authors who are presenting talks have a * after their name.

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